The long-term goal of this project is to identify clinical and genetic features of retinopathy of prematurity (ROP) development, and to analyze their relationships. Although biomedical research data are being generated at an enormous pace, much less work has been done to integrate disparate scientific findings across the spectrum from genomics to imaging to clinical medicine. Our overall hypotheses are that genetic factors are involved in the initiation and modulation of ROP pathogenesis, and that there are etiological relationships among clinical, imaging, and genetic findings in ROP. These hypotheses will be tested using two sequential Specific Aims: (1) Recruit, phenotype, and collect genetic material from a cohort of over 1460 premature infants at- risk for ROP from 7 study centers. Data will be stored in a web-based data management system that will be developed for this project. Demographic and clinical features from three serial ophthalmoscopic examinations will be ascertained fully, and serial wide- angle images will be captured. DNA will be isolated and prepared for genotyping. (2) Quantify retinal vascular features using computer-based image analysis, and analyze relationships between clinical and image findings in ROP. Models for integrating the effects of quantitative image traits, clinical features, and environmental risk factors on ROP susceptibility will be estimated. Genotyping, genetic analysis, recruitment of additional subjects as needed, and modeling of clinical and genetic traits will be pursued during competitive renewal of this project. Ultimately, these studies should improve understanding of neovascularization in ROP and related ocular diseases, and of normal vascular development in infants. In addition, this work should demonstrate a prototype for health information management which combines genotypic and phenotypic data. This project will be performed by a multi-disciplinary team of collaborative investigators with expertise in clinical ophthalmology, biomedical informatics, genetic analysis, and statistical genetics.